An increasing amount of content is being viewed and purchased electronically, such as over the Internet, as opposed to through traditional outlets such as physical “brick and mortar” stores. As the number of electronic retailers offering items or other such content for consumption (e.g., purchase, rent, or download) increases, for example, it is becoming ever more important to properly market and display content to users and potential customers. Traditional display models and campaigns used in physical stores do not always translate well to an electronic environment where varying selections of content to be displayed are often determined dynamically. Physical stores and other relatively static environments can utilize kiosks, large physical displays, end-of-aisle displays, and other such approaches to quickly and easily promote specific items to customers. Such static displays may not perform well in environments where content is selected and/or generated dynamically, such as in response to a user query, however, as there can be little control over the content that will actually be selected. Further, it can be difficult to determine which of the dynamically-selected content should be featured or otherwise prominently displayed.
In an electronic environment such as an electronic marketplace, for example, there is much less space available to present content to the customer, as the customer typically is viewing content in an interface such as a browser on a client device, and typically there will only be one page of information displayed to that customer at any given time. A user navigating in such an environment often will search for certain items by submitting one or more keywords. Various ranking and/or selection algorithms are used to dynamically determine which items to display and/or feature to a customer based at least in part upon the submitted keyword(s). This determination typically involves a number of factors representing different dimensions on how well an item is likely to correspond to a given request. These factors do not necessarily reflect domain-specific knowledge very well, for instance the fact that new high fashion items are more desirable than old high fashion items, or that summer dresses are less desirable in winter than in summer. Further, ranking algorithms often take into account factors such as the popularity of an item when determining which items to display. For items such as high fashion items where there may not be many items sold at any given time, the lag needed to accumulate enough data for the item to rise in the popularity ranking can be longer than the period when the item is actually highly desirable. The inability to optimally select and/or display seasonal content near the beginning of a season can result in a loss of sales, views, or other such actions. While manual changes can be made in some situations, many electronic retailers and other content providers manage many different types of items and other content in various categories, groups, or classes, such that it is not practical to manually manage the seasonal and other such variations.